"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch






class  snake_case__      (       TensorFormatter[Mapping, """torch.Tensor""", Mapping]       ):


 def __init__( self    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
        super().__init__(features=lowerCamelCase						)
        __a       =    torch_tensor_kwargs
        import torch  # noqa import torch at initialization


 def 						a__   ( self    ,		lowerCamelCase						):
        import torch

        if isinstance(lowerCamelCase    ,		lowerCamelCase						) and column:
               if all(
                   isinstance(lowerCamelCase    ,		torch.Tensor						) and x.shape == column[0].shape and x.dtype == column[0].dtype
                   for x in column						):
                      return torch.stack(lowerCamelCase						)
        return column


 def 						a__   ( self    ,		lowerCamelCase						):
        import torch

        if isinstance(lowerCamelCase    ,		(str, bytes, type(lowerCamelCase						))						):
               return value
        elif isinstance(lowerCamelCase    ,		(np.character, np.ndarray)						) and np.issubdtype(value.dtype    ,		np.character						):
               return value.tolist()

        __a       =    {}

        if isinstance(lowerCamelCase    ,		(np.number, np.ndarray)						) and np.issubdtype(value.dtype    ,		np.integer						):
               __a       =    {"dtype": torch.intaa}
        elif isinstance(lowerCamelCase    ,		(np.number, np.ndarray)						) and np.issubdtype(value.dtype    ,		np.floating						):
               __a       =    {"dtype": torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(lowerCamelCase    ,		PIL.Image.Image						):
                      __a       =    np.asarray(lowerCamelCase						)
        return torch.tensor(lowerCamelCase    ,		**{**default_dtype, **self.torch_tensor_kwargs}						)


 def 						a__   ( self    ,		lowerCamelCase						):
        import torch

        # support for torch, tf, jax etc.
        if hasattr(lowerCamelCase    ,		"__array__"						) and not isinstance(lowerCamelCase    ,		torch.Tensor						):
               __a       =    data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowerCamelCase    ,		np.ndarray						):
               if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(lowerCamelCase						) for substruct in data_struct]						)
        elif isinstance(lowerCamelCase    ,		(list, tuple)						):
               return self._consolidate([self.recursive_tensorize(lowerCamelCase						) for substruct in data_struct]						)
        return self._tensorize(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return map_nested(self._recursive_tensorize    ,		lowerCamelCase    ,		map_list=lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.numpy_arrow_extractor().extract_row(lowerCamelCase						)
        __a       =    self.python_features_decoder.decode_row(lowerCamelCase						)
        return self.recursive_tensorize(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.numpy_arrow_extractor().extract_column(lowerCamelCase						)
        __a       =    self.python_features_decoder.decode_column(lowerCamelCase    ,		pa_table.column_names[0]						)
        __a       =    self.recursive_tensorize(lowerCamelCase						)
        __a       =    self._consolidate(lowerCamelCase						)
        return column






 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.numpy_arrow_extractor().extract_batch(lowerCamelCase						)
        __a       =    self.python_features_decoder.decode_batch(lowerCamelCase						)
        __a       =    self.recursive_tensorize(lowerCamelCase						)
        for column_name in batch:
               __a       =    self._consolidate(batch[column_name]						)
        return batch

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
